Variations of compound warm, dry, wet, and cold climate extremes in India during 1951 to 2014.

The simultaneous occurrence of climate extremes significantly impacts ecosystems, increasing the vulnerability to physical risks. Despite extensive research on hot extremes and droughts globally, there remains a significant gap in comprehending the occurrence, magnitude, spatial extent, and associated risks of compound extremes, encompassing scenarios like warm/dry, cold/dry, warm/wet, and cold/wet. This study investigates various compound extreme scenarios by examining combinations of maximum temperature (Tx) and the Standardized Precipitation Evapotranspiration Index (SPEI) using monthly data from 1951 to 2014 acquired from the India Meteorological Department (IMD) for Indian landmass. From the results, the spatial extent of warm/dry events has increased at 1.8 % per decade, while cold/wet events decreased by 1.1 % over India. The warm/wet events have shown an increased trend of about 0.3 %, and cold/dry events at modest rise of 0.7 % per decade. Furthermore, compound warm/dry and cold/wet extremes over India exhibit extreme frequency and shorter return periods, posing greater risk. Conversely, compound cold/dry and warm/wet extremes occur less often, indicating longer return periods and lower risk. Across much of the country, the frequency of warm/dry, cold/dry, warm/wet, and cold/wet extremes ranges from 30 to 45, 15-30, 20-30, and 25-45 months, respectively. Notably, warm/dry conditions exhibit increased frequency in the recent period (1983-2014) with 31 years compared to the base period (1951-1982) which had approximately 24 years for a spatial extent exceeding 5 %. The findings of this study contribute to an enhanced understanding of the changes in compound climate extremes from a multivariate perspective.

Compounded wind gusts and maximum temperature via semiparametric copula in the risk assessments of power blackouts and air conditioning demands for major cities in Canada.

A semiparametric copula joint framework was proposed to model wind gust speed (WGS) and maximum temperature (MT) in Canada, using Gaussian kernel density estimation (GKDE) with parametric copulas. Their joint probability estimates allow for a better understanding of the risk of power blackouts and the demand for air conditioning in the community. The bivariate framework used two extreme sample groups to define extreme pairs at different time lags, i.e., 0 to ± 3 days, annual maximum WGS (AMWGS) and corresponding MT and annual highest MT (AHMT) and corresponding WGS. A thorough model performance comparison indicated that GKDE outperformed the parametric models in defining the marginal distribution of selected univariate series. Significant positive correlations were observed among extreme pairs, except for Calgary and Halifax stations, with inconsistent correlation variations based on selected cities and lag time. Various parametric 2-D copulas were selected to model the dependence structure of bivariate pairs at different time lags for selected stations. AMWGS or AHMT events, when considered independently, would be stressful for all stations due to high estimated quantiles with low univariate RPs. The bivariate events exhibited lower AND-joint RPs with moderate to high design quantiles, indicating a higher risk of power blackouts and heightened air-conditioning demands, which varied inconsistently with time lags across the station. The bivariate AMWGS and corresponding MT events would be stressful in Regina, Quebec City, Ottawa, and Edmonton, while AHMT and corresponding WGS events in Toronto, Regina, and Montreal. Conversely, Vancouver poses a lower risk of joint action of pairs AHMT and corresponding WGS events. These hazard statistics can help in better planning for community well-being during extreme weather.

Probabilistic post-processing of short to medium range temperature forecasts: Implications for heatwave prediction in India.

Accurate and reliable air temperature forecasts are necessary for predicting and responding to thermal disasters such as heat strokes. Forecasts from Numerical Weather Prediction (NWP) models contain biases which require post-processing. Studies assessing the skill of probabilistic post-processing techniques (PPTs) on temperature forecasts in India are lacking. This study aims to evaluate probabilistic post-processing approaches such as Nonhomogeneous Gaussian Regression (NGR) and Bayesian Model Averaging (BMA) for improving daily temperature forecasts from two NWP models, namely, the European Centre for Medium Range Weather Forecasts (ECMWF) and the Global Ensemble Forecast System (GEFS), across the Indian subcontinent. Apart from that, the effect of probabilistic PPT on heatwave prediction skills across India is also evaluated. Results show that probabilistic PPT comprehensively outperform traditional approaches in forecasting temperatures across India at all lead times. In the Himalayan regions where the forecast skill of raw forecasts is low, the probabilistic techniques are not able to produce skillful forecasts even though they perform much better than traditional techniques. The NGR method is found to be the best performing PPT across the Indian region. Post-processing Tmax forecasts using the NGR approach was found to considerably improve the heatwave prediction skill across highly heatwave prone regions in India. The outcomes of this study will be helpful in setting up improved heatwave prediction and early warning systems in India.

Heat tolerance of goats to increased daily maximum temperature and low salinity of drinking water in tropical humid regions.

OBJECTIVE: The daily maximum temperature and seawater level continuously increase as global warming continues. We examined the adaptability and production performance of heat-stressed goats with a supply of low-saline drinking water. METHODS: Twelve Kacang and Kacang Etawah cross goats were exposed to two climatic conditions (control, 25°C to 33°C, 83% relative humidity [RH], temperature humidity index [THI]: 76 to 86; and hot environment, 26°C to 39°C, 81% RH, THI: 77 to 94) and two salt levels in drinking water (0% and 0.4% NaCl). The experimental design was a Latin Square (4×4) with four treatments and four periods (28 days each). RESULTS: Temperature of the rectal, skin, and udder, and respiration rate rose, reached a maximum level on the first day of heat exposures, and then recovered. Plasma sodium rose at 0.4% NaCl level, while the hot environment and salinity treatments increased the drinking water to dry matter (DM) intake ratio. Water excretion was elevated in the hot environment but lowered by the increase in salinity. Total lying time increased, whereas change position frequency decreased in the hot condition. Lying and ruminating and total ruminating time increased and explained the enhanced DM digestibility in the hot conditions. CONCLUSION: The goats exhibited a high level of plasma sodium as salinity increased, and they demonstrated physiological and behavioral alterations while maintaining their production performances under increasing daily maximum temperatures.

Numerical Study on a Liquid Cooling Plate with a Double-Layer Minichannel for a Lithium Battery Module.

The liquid cooling system of lithium battery modules (LBM) directly affects the safety, efficiency, and operational cost of lithium-ion batteries. To meet the requirements raised by a factory for the lithium battery module (LBM), a liquid cooling plate with a two-layer minichannel heat sink has been proposed to maintain temperature uniformity in the module and ensure it stays within the temperature limit. This innovative design features a single inlet and a single outlet. To evaluate the performance of the liquid cooling system, we considered various discharge rates while taking into account the structure, flow rate, and temperature of the coolant. Our findings indicate that at a mass outflow rate of 20 g/s, a better cooling effect and lower power consumption can be achieved. An inlet temperature of 20 °C, close to the initial temperature of the battery string, may be the most appropriate because a higher temperature of the coolant will cause a higher temperature of LBM, so far as to exceed the safe threshold value. In the case of larger rate discharge, the design of a double-layer MCHS at the bottom and an auxiliary one at the side can effectively reduce the maximum temperature LBM (within 28 °C) and maintain the temperature difference in the single cell at approximately 4 °C. In the case of non-constant discharges, the temperature difference between cells increases with the maximum temperature. When the discharge rate is reduced, the large temperature difference helps the temperature to drop rapidly. This can provide guidance for the design of cooling systems for the LBM.

Impact of desert dust storms, PM10 levels and daily temperature on mortality and emergency department visits due to stroke.

Objective: It is known that the inhalation of air pollutants adversely affects human health. These air pollutants originated from natural sources such as desert storms or human activities including traffic, power generating, domestic heating, etc. This study aimed to investigate the impacts of desert dust storms, particulate matter ≤10 µm (PM10) and daily maximum temperature (MT) on mortality and emergency department (ED) visits due to stroke in the city of Gaziantep, Southeast Turkey. Method: The data on mortality and ED visits due to stroke were retrospectively recruited from January 1, 2009, to March 31, 2014, in Gaziantep City Centre. Results: PM10 levels did not affect ED visits or mortality due to stroke; however, MT increased both ED visits [adjusted odds ratio (OR) = 1.002, 95% confidence interval (CI) = 1.001-1.003] and mortality (OR = 1.006, 95% CI = 0.997-1.014) due to stroke in women. The presence of desert storms increased ED visits due to stroke in the total population (OR = 1.219, 95% CI = 1.199-1.240), and all subgroups. It was observed that desert dust storms did not have an increasing effect on mortality. Conclusion: Our findings suggest that MT and desert dust storms can induce morbidity and mortality due to stroke.

Investigation on the size and percentage effects of magnesium nanoparticles on thermophysical properties of reinforced calcium phosphate bone cement by molecular dynamic simulation.

In recent years, bone materials and cement innovation have made extraordinary strides. Calcium phosphate cement (CPC) regenerates body tissues and repairs bone and dental defects. Since the presence of nanoparticles (NPs) increased the initial cement strength in terms of the reduction of porosity, magnesium (Mg) NPs were used because of their unique properties. In this study, the effects of various Mg NP percentages and sizes on reinforced cement thermal behavior and mechanical behavior are investigated using the molecular dynamics (MD) simulation method. The changes of Young's modulus (YM), maximum temperature (MT), and ultimate strength (US) were investigated for this reason. The US, YM, and MT of the reinforced cement sample improved from 0.879 to 0.171 MPa to 1.326 and 0.255 MPa, respectively, and from 1321 to 1403 K by raising the NPs percentage to 4%. The radius increase of NPs to 16 Å enhanced the US, YM, and MT to 0.899 MPa, 0.179 MPa, and 1349 K. The MT decreased to 1275 K. The quantity and size of the Mg NPs significantly enhanced the mechanical behavior of the finished cement, according to the findings.

Physiological changes and behavioral responses in heat-stressed goats under humid tropical environment.

Kacang (K) and Kacang Etawah (KE) cross goats are the major goat breeds that are important for farming livelihood and income and recognized for their tolerance to hot and humid tropical climates. As global warming progresses, the daily maximum temperature (Tmax) is predicted to be continuously increased, which will challenge goat production in the future. The objective of this study was to evaluate the physiological and behavioral responses of the goats to the elevated Tmax. Six K and six KE female goats were housed in a normal environment (average Tmax: 33°C; temperature humidity index (THI): 76 to 86) for 6 weeks and then in a hot environment (average Tmax: 38°C; THI: 76 to 92) for 7 weeks. During hot conditions, rectal, rectal surface, and skin temperature, respiration rate, hemoglobin, and cholesterol increased (p < 0.05), whereas glucose blood levels decreased (p < 0.01). Dry matter (DM) intake was lowered (p < 0.01), and DM digestibility was elevated (p < 0.01), whereas drinking water and body water retention were lowered but varied (p < 0.05) during hot weeks. Lying time increased during the hot weeks in both breeds (p < 0.01), whereas lying and ruminating as well as total ruminating time was longer (p < 0.05) in KE goats compared to K goats, which explain the greater (p < 0.05) DM digestibility in KE goats. The effect of the elevated Tmax might be less severe since it also depends on the duration of the Tmax and the variation of daily THI.

Quantifying the influence of climate, host and change in land-use patterns on occurrence of Crimean Congo Hemorrhagic Fever (CCHF) and development of spatial risk map for India.

Crimean Congo Hemorrhagic Fever (CCHF), is an emerging zoonosis globally and in India. The present study focused on identifying the risk factors for occurrence of CCHF in the Indian state of Gujarat and development of risk map for India. The past CCHF outbreaks in India were collated for the analyses. Influence of land use change and climatic factors in determining the occurrence of CCHF in Gujarat was assessed using Bayesian spatial models. Change in maximum temperature in affected districts was analysed to identify the significant change points over 110 years. Risk map was developed for Gujarat using Bayesian Additive Regression Trees (BART) model with remotely sensed environmental variables and host (livestock and human) factors. We found the change in land use patterns and maximum temperature in affected districts to be contributing to the occurrence of CCHF in Gujarat. Spatial risk map developed using CCHF occurrence data for Gujarat identified density of buffalo, minimum land surface temperature and elevation as risk determinants. Further, spatial risk map for the occurrence of CCHF in India was developed using selected variables. Overall, we found that combination of factors such as change in land-use patterns, maximum temperature, buffalo density, day time minimum land surface temperature and elevation led to the emergence and further spread of the disease in India. Mitigation measures for CCHF in India could be designed considering disease epidemiology and initiation of surveillance strategies based on the risk map developed in this study.

Effect of abnormal values of three temperature indicators on ischemic stroke hospital admissions in Guangzhou, China.

Abnormal temperature has important effects on the occurrence of ischemic stroke (IS). However, relatively less efforts have been taken to systematically unravel the association between various abnormal temperature and IS hospital admission. Focusing on three temperature indicators (i.e., mean temperature, maximum temperature, and minimum temperature), this study attempts to analyse how their abnormal values affect IS hospital admission. The dataset covers the period between September 17, 2012 and August 28, 2018, and includes a total of 1464 cases who were admitted to the hospital for the first onset of IS and lived in the main urban area of Guangzhou. The study adopts the time-stratified case-crossover analysis. Abnormal values of temperature were measured using the 2.5th and 97.5th quantile values of each temperature indicator, with the former refers to a low value whereas the latter a high one. The effects of abnormal temperature on IS hospital admission were assessed through calculating the relative risks induced by the low and high values (the median values of each temperature indicators were taken as the references). The results show that the risk window periods for IS hospital admission associated with the low values of the temperature indicators are the lags of 3-7 days and 18-19 days. The risks of high temperature values on IS admission, however, are insignificant with either one-day lag or cumulative lag. As to different population groups, females show higher risks of IS hospital admission at low temperature values than males; and elderly people, compared with young people, are more vulnerable to low temperature values. To cities with similar climate of Guangzhou, particular attention should be paid to the impact of low temperature values, especially the low value of minimum temperature, on IS admission, and to females and elderly people who are more sensitive to abnormal temperatures.

Short-Term Effects of Climate Variability on Childhood Diarrhoea in Bangladesh: Multi-Site Time-Series Regression Analysis.

The aim of this study was to estimate the effects of climate on childhood diarrhoea hospitalisations across six administrative divisions in Bangladesh and to provide scientific evidence for local health authorities for disease control and prevention. Fortnightly hospital admissions (August/2013-June/2017) for diarrhoea in children under five years of age, and fortnightly average maximum temperature, relative humidity and rainfall recordings for six administrative divisions were modelled using negative binomial regression with distributed lag linear terms. Flexible spline functions were used to adjust models for seasonality and long-term trends. During the study period, 25,385 diarrhoea cases were hospitalised. Overall, each 1 °C rise in maximum temperature increased diarrhoea hospitalisations by 4.6% (IRR = 1.046; 95% CI, 1.007-1.088) after adjusting for seasonality and long-term trends in the unlagged model. Using lagged effects of maximum temperature, and adjusting for relative humidity and rainfall for each of the six administrative divisions, the relationship between maximum temperature and diarrhoea hospitalisations varied between divisions, with positive and negative effect estimates. The temperature-diarrhoea association may be confounded by seasonality and long-term trends. Our findings are a reminder that the effects of climate change may be heterogeneous across regions, and that tailored diarrhoea prevention strategies need to consider region-specific recommendations rather than relying on generic guidelines.

Body mass decline in a Mediterranean community of solitary bees supports the size shrinking effect of climatic warming.

The long-known, widely documented inverse relationship between body size and environmental temperature ("temperature-size rule") has recently led to predictions of body size decline following current climatic warming ("size shrinking effect"). For keystone pollinators such as wild bees, body shrinking in response to warming can have significant effects on pollination processes but there is still little direct evidence of the phenomenon because adequate tests require controlling for confounding factors linked to climate change (e.g., habitat change). This paper assesses the shrinking effect in a community of solitary bees from well-preserved habitats in the core of a large nature reserve experiencing climatic warming without disturbances or habitat changes. Long-term variation in mean body mass was evaluated using data from 1704 individual bees (137 species, 27 genera, 6 families) sampled over 1990-2023. Climate warmed at a fast rate during this period, annual mean of daily maximum temperature increasing 0.069°C/year on average during 2000-2020. Changes in bee body mass verified expectations from the size shrinking effect. The mean individual body mass of the community of solitary bees declined significantly, irrespective of whether the analysis referred to the full species sample or only to the subset of species that were sampled in both the old (1990-1997) and recent (2022-2023) periods. On average, body mass declined ~0.7%·year-1 , or an estimated average cumulative reduction of ~20 mg per individual bee from 1990 to 2023. Proportional size reduction was greatest among large-bodied species, ranging from around -0.6%·year-1 for the smallest species to -0.9%·year-1 for the largest ones. Declining rate was steeper for cavity-nesting than ground-nesting species. The pollination and mating systems of bee-pollinated plants in the study region are probably undergoing significant alterations as a consequence of supra-annual decline in bee body mass.

[Quantitative Analysis of Spatio-temporal Evolution Characteristics of Seasonal Average Maximum Temperature and Its Influence by Atmospheric Circulation in China from 1950 to 2019].

Global warming and intensified human activities have led to regional climate instability with increasing frequency and the persistence of high-temperature climate events. Eco-environmental protection and socio-economic development have been faced with rigorous threats. Taking the monthly maximum temperatures from 1950 to 2019 as the basic data source, the spatial-temporal evolution characteristics of seasonal average maximum temperature (AMT) were discerned using the Mann-Kendall test and unary linear regression method in China from 1950 to 2019. Combined with linear correlation, partial linear correlation, and wavelet analysis, the correlation between seasonal AMT characteristics and atmospheric circulations was analyzed quantitatively. The results showed that:① the AMT in all seasons had a significant upward trend, with an increase of 1.21, 0.08, 1.81, and 0.25℃ in spring, summer, autumn, and winter, respectively. The abrupt change times of the AMT were concentrated in the 1990s to the early 21st century. ② In terms of spatial distribution, except for in summer, the average trend rates of AMT in other seasons increased gradually from south to north, although the increasing degrees were different. Among them, the AMT change rate in spring-winter was the fastest in northeast and northwest China. ③ There were complex correlations between the AMT of every season and atmospheric circulation factors, and the distribution of the interrelation energy varied significantly in different frequency domains. Specifically, the Pacific Decadal Oscillation had a significant negative correlation with AMT in summer. The North Atlantic Oscillation had an active effect on AMT changes in summer, autumn, and winter. The Arctic Oscillation had a significant positive driving effect on AMT in all seasons, and there were significant positive or negative influences on the short-or long-term changes of AMT in spring and summer due to the different EI Niño-Southern Oscillation years. These results could provide a theoretical basis and technical reference for China to formulate scientific and effective response plans of climate change.

Calculation of the Maximum Temperature of Diester-Based Magnetic Fluid Layers in High-Speed Seals.

Magnetic fluids, as smart nanomaterials, have been successfully used in sealing applications and other fields. However, the temperature of magnetic fluids in the sealing gap is a key factor affecting sealing performances, limiting their application in high-speed sealing fields. Since obtaining a direct measurement of the magnetic fluid's temperature is difficult, due to the small clearance, accurately calculating the maximum temperature of the magnetic fluid layer in high-speed seals is crucial. Herein, a mathematical model for calculating the maximum temperature of the magnetic fluid layer was established, by using a reasonable simplification of high-speed sealing conditions, and the calculation formula was modified by studying the rheological properties of the diester-based magnetic fluid. The results suggest that the calculation of the maximum temperature is influenced by viscous dissipation, and both are related to the rheological characteristics of magnetic fluids. When the influence of rheological properties is ignored, the calculation results are not accurate for higher-velocity seals, but the calculation model applies to lower-velocity seals. When the influence of rheological properties is considered, the calculation results obtained by the corrected formula are more accurate, and they are applicable to both lower- and higher-velocity seals. This work can help us more accurately and conveniently estimate the maximum temperature of magnetic fluids in high-speed seal applications, which is of theoretical and practical research significance for determining sealing performances and thermal designs.

Substantial warming of Central European mountain rivers under climate change.

Water bodies around the world are currently warming with unprecedented rates since observations started, but warming occurs highly variable among ecoregions. So far, mountain rivers were expected to experience attenuated warming due to cold water input from snow or ice. However, air temperatures in mountain areas are increasing faster than the global average, and therefore warming effects are expected for cold riverine ecosystems. In decomposing multi-decadal water temperature data of two Central European mountain rivers with different discharge and water source regime, this work identified so far unreported (a) long-term warming trends (with river-size dependent rates between +0.24 and +0.44 °C decade-1); but also (b) seasonal shifts with both rivers warming not only during summer, but also in winter months (i.e., up to +0.52 °C decade-1 in November); (c) significantly increasing minimum and maximum temperatures (e.g., temperatures in a larger river no longer reach freezing point since 1996 and maximum temperatures increased at rates between +0.4 and +0.7 °C decade-1); and (d) an expanding of warm-water periods during recent decades in these ecosystems. Our results show a substantial warming effect of mountain rivers with significant month-specific warming rates not only during summer but also in winter, suggesting that mountain river phenology continues to change with ongoing atmospheric warming. Furthermore, this work demonstrates that apart from a general warming, also seasonal shifts, changes in extreme temperatures, and expanding warm periods will play a role for ecological components of mountain rivers and should be considered in climate change assessments and mitigation management. Supplementary Information: The online version contains supplementary material available at 10.1007/s10113-023-02037-y.

Analysis of heatwave characteristics under climate change over three highly populated cities of South India: a CMIP6-based assessment.

Climate change is arguably the most alarming global concern of the twenty-first century, particularly due to the increased frequency of meteorological extremes, e.g., heatwaves, droughts, and floods. Heatwaves are considered a potential health risk and urge further study, robust preparedness, and policy framing. This study presents an analysis of heatwave characteristics for historical (1980-2014), near-future (2021-2055), and far-future (2056-2090) scenarios over three highly populated cities of South India, i.e., Bangalore, Chennai, and Hyderabad. Two different approaches, i.e., the India Meteorological Department (IMD) criterion and the percentile-based criterion, are considered for defining the threshold of a heatwave day. Nine general circulation models (GCMs) from the Coupled Model Inter-comparison Project phase 6 (CMIP6) experiment are selected, evaluated after bias correction, and the best performer was utilized to obtain the temperature projections corresponding to two shared socioeconomic pathways (SSP 2-4.5 and 5-8.5) for the future periods. The results reveal a high frequency of heatwave days over the cities in recent years from both approaches, which may further exacerbate in the future, thereby putting a large population at risk. The number of heatwave days is much higher for SSP5-8.5 than that for SSP2-4.5, depicting the direct effects of anthropogenic activities on the frequency of heatwaves. The detailed analysis of heatwave projections will help develop equitable heat resilient mitigation and adaptation strategies for the future, thereby alleviating their pernicious impacts.

Daily Estimation of Global Solar Irradiation and Temperatures Using Artificial Neural Networks through the Virtual Weather Station Concept in Castilla and León, Spain.

In this article, the interpolation of daily data of global solar irradiation, and the maximum, average, and minimum temperatures were measured. These measurements were carried out in the agrometeorological stations belonging to the Agro-climatic Information System for Irrigation (SIAR, in Spanish) of the Region of Castilla and León, in Spain, through the concept of Virtual Weather Station (VWS), which is implemented with Artificial Neural Networks (ANNs). This is serving to estimate data in every point of the territory, according to their geographic coordinates (i.e., longitude and latitude). The ANNs of the Multilayer Feed-Forward Perceptron (MLP) used are daily trained, along with data recorded in 53 agro-meteorological stations, and where the validation of the results is conducted in the station of Tordesillas (Valladolid). The ANN models for daily interpolation were tested with one, two, three, and four neurons in the hidden layer, over a period of 15 days (from 1 to 15 June 2020), with a root mean square error (RMSE, MJ/m2) of 1.23, 1.38, 1.31, and 1.04, respectively, regarding the daily global solar irradiation. The interpolation of ambient temperature also performed well when applying the VWS concept, with an RMSE (°C) of 0.68 for the maximum temperature with an ANN of four hidden neurons, 0.58 for the average temperature with three hidden neurons, and 0.83 for the minimum temperature with four hidden neurons.

Potential Impacts of Different Occupational Outdoor Heat Exposure Thresholds among Washington State Crop and Construction Workers and Implications for Other Jurisdictions.

Occupational heat exposure is associated with substantial morbidity and mortality among outdoor workers. We sought to descriptively evaluate spatiotemporal variability in heat threshold exceedances and describe potential impacts of these exposures for crop and construction workers. We also present general considerations for approaching heat policy-relevant analyses. We analyzed county-level 2011-2020 monthly employment (Bureau of Labor Statistics Quarterly Census of Employment and Wages) and environmental exposure (Parameter-elevation Relationships on Independent Slopes Model (PRISM)) data for Washington State (WA), USA, crop (North American Industry Classification System (NAICS) 111 and 1151) and construction (NAICS 23) sectors. Days exceeding maximum daily temperature thresholds, averaged per county, were linked with employment estimates to generate employment days of exceedances. We found spatiotemporal variability in WA temperature threshold exceedances and crop and construction employment. Maximum temperature exceedances peaked in July and August and were most numerous in Central WA counties. Counties with high employment and/or high numbers of threshold exceedance days, led by Yakima and King Counties, experienced the greatest total employment days of exceedances. Crop employment contributed to the largest proportion of total state-wide employment days of exceedances with Central WA counties experiencing the greatest potential workforce burden of exposure. Considerations from this analysis can help inform decision-making regarding thresholds, timing of provisions for heat rules, and tailoring of best practices in different industries and areas.

Severity of intrapartum fever and neonatal outcomes.

BACKGROUND: The few studies that have addressed the relationship between severity of intrapartum fever and neonatal and maternal morbidity have had mixed results. The impact of the duration between reaching maximum intrapartum temperature and delivery on neonatal outcomes remains unknown. OBJECTIVE: To test the association of severity of intrapartum fever and duration from reaching maximum temperature to delivery with neonatal and maternal morbidity. STUDY DESIGN: This was a secondary analysis of a prospective cohort of term, singleton patients admitted for induction of labor or spontaneous labor who had intrapartum fever (≥38°C). Patients were divided into 3 groups according to maximum temperature during labor: afebrile (<38°C), mild fever (38°C-39°C), and severe fever (>39°C). The primary outcome was composite neonatal morbidity (umbilical artery pH <7.1, mechanical ventilation, respiratory distress, meconium aspiration with pulmonary hypertension, hypoglycemia, neonatal intensive care unit admission, and Apgar <7 at 5 minutes). Secondary outcomes were composite neonatal neurologic morbidity (hypoxic-ischemic encephalopathy, hypothermia treatment, and seizures) and composite maternal morbidity (postpartum hemorrhage, endometritis, and maternal packed red blood cell transfusion). Outcomes were compared between the maximum temperature groups using multivariable logistic regression. Cox proportional-hazards regression modeling accounted for the duration between reaching maximum intrapartum temperature and delivery. RESULTS: Of the 8132 patients included, 278 (3.4%) had a mild fever and 74 (0.9%) had a severe fever. The incidence of composite neonatal morbidity increased with intrapartum fever severity (afebrile 5.4% vs mild 18.0% vs severe 29.7%; P<.01). After adjusting for confounders, there were increased odds of composite neonatal morbidity with severe fever compared with mild fever (adjusted odds ratio, 1.93 [95% confidence interval, 1.07-3.48]). Severe fevers remained associated with composite neonatal morbidity compared with mild fevers after accounting for the duration between reaching maximum intrapartum temperature and delivery (adjusted hazard ratio, 2.05 [95% confidence interval, 1.23-3.43]). Composite neonatal neurologic morbidity and composite maternal morbidity were not different between patients with mild and patients with severe fevers. CONCLUSION: Composite neonatal morbidity correlated with intrapartum fever severity in a potentially dose-dependent fashion. This correlation was independent of the duration from reaching maximum intrapartum temperature to delivery, suggesting that clinical management of intrapartum fever, in terms of timing or mode of delivery, should not be affected by this duration.

Disentangling climatic and nest predator impact on reproductive output reveals adverse high-temperature effects regardless of helper number in an arid-region cooperative bird.

Climate exerts a major influence on reproductive processes, and an understanding of the mechanisms involved and which factors might mitigate adverse weather is fundamental under the ongoing climate change. Here, we study how weather and nest predation influence reproductive output in a social species, and examine whether larger group sizes can mitigate the adverse effects of these factors. We used a 7-year nest predator-exclusion experiment on an arid-region cooperatively breeding bird, the sociable weaver. We found that dry and, especially, hot weather were major drivers of nestling mortality through their influence on nest predation. However, when we experimentally excluded nest predators, these conditions were still strongly associated with nestling mortality. Group size was unimportant against nest predation and, although positively associated with reproductive success, it did not mitigate the effects of adverse weather. Hence, cooperative breeding might have a limited capacity to mitigate extreme weather effects.